Athletes participating in elite sports are exposed to high training loads and increasingly saturated competition calendars. Emerging evidence indicates that poor load management is a major risk factor for injury. The International Olympic Committee convened an expert group to review the scientific evidence for the relationship of load (defined broadly to include rapid changes in training and competition load, competition calendar congestion, psychological load and travel) and health outcomes in sport. We summarise the results linking load to risk of injury in athletes, and provide athletes, coaches and support staff with practical guidelines to manage load in sport. This consensus statement includes guidelines for (1) prescription of training and competition load, as well as for (2) monitoring of training, competition and psychological load, athlete well-being and injury. In the process, we identified research priorities.

https://bjsm.bmj.com/content/bjsports/50/17/1030.full.pdf

How much is too much? (Part 2) International Olympic Committee consensus statement on load in sport and risk of illness

Monitoring the load placed on athletes in both training and competition has become a very hot topic in sport science. Both scientists and coaches routinely monitor training loads using multidisciplinary approaches, and the pursuit of the best methodologies to capture and interpret data has produced an exponential increase in empirical and applied research. Indeed, the field has developed with such speed in recent years that it has given rise to industries aimed at developing new and novel paradigms to allow us to precisely quantify the internal and external loads placed on athletes and to help protect them from injury and ill health. In February 2016, a conference on "Monitoring Athlete Training Loads-The Hows and the Whys" was convened in Doha, Qatar, which brought together experts from around the world to share their applied research and contemporary practices in this rapidly growing field and also to investigate where it may branch to in the future. This consensus statement brings together the key findings and recommendations from this conference in a shared conceptual framework for use by coaches, sport-science and -medicine staff, and other related professionals who have an interest in monitoring athlete training loads and serves to provide an outline on what athlete-load monitoring is and how it is being applied in research and practice, why load monitoring is important and what the underlying rationale and prospective goals of monitoring are, and where athlete-load monitoring is heading in the future.

/pdf/monitoring-athlete-training-loads-consensus-statement-3fhlq6hr7y.pdf

Monitoring Athlete Training Loads: Consensus Statement.

Background
Coaches, sport scientists, clinicians and medical personnel face a constant challenge to prescribe sufficient training load to produce training adaption while minimising fatigue, performance inhibition and risk of injury/illness.

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Training load and fatigue marker associations with injury and illness: A systematic review of longitudinal studies

The etiology of running-related injury is important to consider as the effectiveness of a given running-related injury prevention intervention is dependent on whether etiologic factors are readily modifiable and consistent with a biologically plausible causal mechanism. Therefore, the purpose of the present article was to present an evidence-informed conceptual framework outlining the multifactorial nature of running-related injury etiology. In the framework, four mutually exclusive parts are presented: (a) Structure-specific capacity when entering a running session; (b) structure-specific cumulative load per running session; (c) reduction in the structure-specific capacity during a running session; and (d) exceeding the structure-specific capacity. The framework can then be used to inform the design of future running-related injury prevention studies, including the formation of research questions and hypotheses, as well as the monitoring of participation-related and non-participation-related exposures. In addition, future research applications should focus on addressing how changes in one or more exposures influence the risk of running-related injury. This necessitates the investigation of how different factors affect the structure-specific load and/or the load capacity, and the dose-response relationship between running participation and injury risk. Ultimately, this direction allows researchers to move beyond traditional risk factor identification to produce research findings that are not only reliably reported in terms of the observed cause-effect association, but also translatable in practice.

A framework for the etiology of running‐related injuries

Injuries have a great impact on professional soccer, due to their large influence on team performance and the considerable costs of rehabilitation for players Existing studies in the literature provide just a preliminary understanding of which factors mostly affect injury risk, while an evaluation of the potential of statistical models in forecasting injuries is still missing In this paper, we propose a multi-dimensional approach to injury forecasting in professional soccer that is based on GPS measurements and machine learning By using GPS tracking technology, we collect data describing the training workload of players in a professional soccer club during a season We then construct an injury forecaster and show that it is both accurate and interpretable by providing a set of case studies of interest to soccer practitioners Our approach opens a novel perspective on injury prevention, providing a set of simple and practical rules for evaluating and interpreting the complex relations between injury risk and training performance in professional soccer

/pdf/effective-injury-forecasting-in-soccer-with-gps-training-3plty24i02.pdf

Effective injury forecasting in soccer with GPS training data and machine learning.

Objective To determine if any differences exist between the rolling averages and exponentially weighted moving averages (EWMA) models of acute:chronic workload ratio (ACWR) calculation and subsequent injury risk.

Methods A cohort of 59 elite Australian football players from 1 club participated in this 2-year study. Global positioning system (GPS) technology was used to quantify external workloads of players, and non-contact ‘time-loss’ injuries were recorded. The ACWR were calculated for a range of variables using 2 models: (1) rolling averages, and (2) EWMA. Logistic regression models were used to assess both the likelihood of sustaining an injury and the difference in injury likelihood between models.

Results There were significant differences in the ACWR values between models for moderate (ACWR 1.0–1.49; p=0.021), high (ACWR 1.50–1.99; p=0.012) and very high (ACWR >2.0; p=0.001) ACWR ranges. Although both models demonstrated significant (p 2.0) and an increase in injury risk for total distance ((relative risk, RR)=6.52–21.28) and high-speed distance (RR=5.87–13.43), the EWMA model was more sensitive for detecting this increased risk. The variance (R2) in injury explained by each ACWR model was significantly (p<0.05) greater using the EWMA model.

Conclusions These findings demonstrate that large spikes in workload are associated with an increased injury risk using both models, although the EWMA model is more sensitive to detect increases in injury risk with higher ACWR.

https://bjsm.bmj.com/content/bjsports/51/9/749.full.pdf

Calculating acute:chronic workload ratios using exponentially weighted moving averages provides a more sensitive indicator of injury likelihood than rolling averages

A model that takes into account the current workload, and the workload the athlete has been prepared for, as an acute:chronic workload ratio has been previously used as a novel way to monitor training load and injury risk. Fifty-nine elite Australian football players from one club participated in this 2-year study. Global Positioning System technology was used to provide information on running workloads of players. An injury was defined as any non-contact 'time-loss' injury. One-week (acute), along with 4-week (chronic) workloads were calculated for a range of variables. The size of the acute workload in relation to the chronic workload was calculated as an acute:chronic workload ratio. An acute:chronic workload ratio of >2.0 for total distance during the in-season was associated with a 5 to 8-fold greater injury risk in the current [relative risk (RR) = 8.65, P = 0.001] and subsequent week (RR = 5.49, P = 0.016). Players with a high-speed distance acute:chronic workload ratio of >2.0 were 5-11 times more likely to sustain an injury in the current (RR = 11.62, P = 0.006) and subsequent week (RR = 5.10, P = 0.014). These findings demonstrate that sharp increases in running workload increase the likelihood of injury in both the week the workload is performed, and the subsequent week.

Individual and combined effects of acute and chronic running loads on injury risk in elite Australian footballers

In recent years, there has been a large expansion in literature pertaining to the game of Australian football (AF). Furthermore, there have been a number of rule changes that are likely to have changed the demands of the game. Based on these advances and changes, it seemed important to conduct a review assessing the scientific literature surrounding the sport. The review evaluates the match demands of AF, the qualities required for success, and the impact training and competition have on adaptation, injury and fatigue. A systematic search of PubMed, CINAHL, SPORTDiscus, Web of Science and Scopus for AF literature was conducted; studies investigating match demands, physical qualities, training practices and injury were included. Weighted means and standard deviations were calculated for match demands and physical and anthropometric profiles across playing standards. A total of 1830 articles were retrieved in the initial search, with 888 removed as duplicates, 626 removed for being non-relevant and a further 152 removed for being AF papers but not relevant to the review. As such, 164 AF papers were included in the review. Due to the intermittent high-intensity nature of match-play, players need a wide range of physical and technical qualities to excel, with speed, aerobic fitness, reactive agility and well-developed lean mass being central to success. Training for AF at the elite level is associated with high workloads, with players engaging in numerous training modalities; even altitude and heat training camps have been utilised by Australian Football League (AFL) teams to further augment fitness improvements. While high chronic workloads can be tolerated and are needed for improving physical qualities, careful planning and monitoring of internal and external workloads is required to minimise sharp spikes in load that are associated with injury. There is a complex interaction between numerous contextual factors that influence the match demands that are discussed in this review. Whilst players must have the physical capacities to cope with the intense physical demands of AF matches, the successful execution of technical skills during match-play is central to success. To develop these skills and attributes, specific and carefully planned and monitored training must be performed over a number of years.

Applied sport science of Australian football: A systematic review

Professional rugby league competition does not coincide with a standardized amount of recovery between matches; matches can be separated by as many as 10 days and as few as 5 days. These variations in recovery time could influence the match activity profiles and injury rates of players. This study investigated the effect of different between-match recovery times on the activity profiles and injury rates of National Rugby League (NRL) players. Forty-three elite male rugby league players participated in this study. Between-match recovery cycles were defined as short (separated by 5 or 6 days), medium (separated by 7 or 8 days), and long (separated by 9 or 10 days) recovery. Movement was recorded using a commercially available microtechnology unit, which provided information on speed, distance, and repeated high-intensity effort activity. Injuries sustained in either training or match play, which resulted in a missed match, were recorded. Significantly greater (p ≤ 0.05) relative total distance was covered after matches involving short recovery than those involving medium (effect size [ES] = 1.13) or long (ES = 1.08) recovery periods. This difference was because of greater low-speed activity. Injury rates for the adjustables positional group were the highest after short between-match recovery cycles, whereas the injury rates of hit-up forwards and outside backs positional groups were the highest after long between-match recovery cycles. These findings suggest that the activity profiles of NRL match play and the injury rates of specific playing positions are influenced by the amount of recovery between matches. The differences in the activity profiles and injury rates between short, medium, and long between-match recovery cycles should be considered when developing recovery strategies for professional rugby league players.

Effect of different between-match recovery times on the activity profiles and injury rates of national rugby league players.

Objectives:To investigate the relationship between the proportion of preseason training sessions completed and load and injury during the ensuing Australian Football League season. Design:Single-cohort, observational study. Methods:Forty-six elite male Australian football players from 1 club participated. Players were divided into 3 equal groups based on the amount of preseason training completed (high [HTL], >85% sessions completed; medium [MTL], 50–85% sessions completed; and low [LTL], <50% sessions completed). Global positioning system (GPS) technology was used to record training and game loads, with all injuries recorded and classified by club medical staff. Differences between groups were analyzed using a 2-way (group × training/competition phase) repeated-measures ANOVA, along with magnitude-based inferences. Injury incidence was expressed as injuries per 1000 h. Results:The HTL and MTL groups completed a greater proportion of in-season training sessions (81.1% and 74.2%) and matches (76.7% and 76....

/pdf/relationship-between-preseason-training-load-and-in-season-14c2r6kh1l.pdf

Nick B. Murray

Papers

Calculating acute:chronic workload ratios using exponentially weighted moving averages provides a more sensitive indicator of injury likelihood than rolling averages

Individual and combined effects of acute and chronic running loads on injury risk in elite Australian footballers

Applied sport science of Australian football: A systematic review

Effect of different between-match recovery times on the activity profiles and injury rates of national rugby league players.

Relationship Between Preseason Training Load and In-Season Availability in Elite Australian Football Players